Literature DB >> 24581797

A comparison of muscle energy models for simulating human walking in three dimensions.

Ross H Miller1.   

Abstract

The popular Hill model for muscle activation and contractile dynamics has been extended with several different formulations for predicting the metabolic energy expenditure of human muscle actions. These extended models differ considerably in their approach to computing energy expenditure, particularly in their treatment of active lengthening and eccentric work, but their predictive abilities have never been compared. In this study, we compared the predictions of five different Hill-based muscle energy models in 3D forward dynamics simulations of normal human walking. In a data-tracking simulation that minimized muscle fatigue, the energy models predicted metabolic costs that varied over a three-fold range (2.45-7.15 J/m/kg), with the distinction arising from whether or not eccentric work was subtracted from the net heat rate in the calculation of the muscle metabolic rate. In predictive simulations that optimized neuromuscular control to minimize the metabolic cost, all five models predicted similar speeds, step lengths, and stance phase durations. However, some of the models predicted a hip circumduction strategy to minimize metabolic cost, while others did not, and the accuracy of the predicted knee and ankle angles and ground reaction forces also depended on the energy model used. The results highlights the need to clarify how eccentric work should be treated when calculating muscle energy expenditure, the difficulty in predicting realistic metabolic costs in simulated walking even with a detailed 3D musculoskeletal model, the potential for using such models to predict energetically-optimal gait modifications, and the room for improvement in existing muscle energy models and locomotion simulation frameworks.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Forward dynamics; Gait; Hill muscle model; Metabolic cost

Mesh:

Year:  2014        PMID: 24581797     DOI: 10.1016/j.jbiomech.2014.01.049

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  26 in total

1.  Walking on a moving surface: energy-optimal walking motions on a shaky bridge and a shaking treadmill can reduce energy costs below normal.

Authors:  Varun Joshi; Manoj Srinivasan
Journal:  Proc Math Phys Eng Sci       Date:  2015-02-08       Impact factor: 2.704

2.  Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement.

Authors:  Jennifer L Hicks; Thomas K Uchida; Ajay Seth; Apoorva Rajagopal; Scott L Delp
Journal:  J Biomech Eng       Date:  2015-01-26       Impact factor: 2.097

3.  Muscle-tendon mechanics explain unexpected effects of exoskeleton assistance on metabolic rate during walking.

Authors:  Rachel W Jackson; Christopher L Dembia; Scott L Delp; Steven H Collins
Journal:  J Exp Biol       Date:  2017-03-24       Impact factor: 3.312

4.  Metabolic cost underlies task-dependent variations in motor unit recruitment.

Authors:  Adrian K M Lai; Andrew A Biewener; James M Wakeling
Journal:  J R Soc Interface       Date:  2018-11-21       Impact factor: 4.118

5.  Development of a Subject-Specific Foot-Ground Contact Model for Walking.

Authors:  Jennifer N Jackson; Chris J Hass; Benjamin J Fregly
Journal:  J Biomech Eng       Date:  2016-09-01       Impact factor: 2.097

6.  Modeling toes contributes to realistic stance knee mechanics in three-dimensional predictive simulations of walking.

Authors:  Antoine Falisse; Maarten Afschrift; Friedl De Groote
Journal:  PLoS One       Date:  2022-01-25       Impact factor: 3.240

7.  Predictive neuromechanical simulations indicate why walking performance declines with ageing.

Authors:  Seungmoon Song; Hartmut Geyer
Journal:  J Physiol       Date:  2018-03-02       Impact factor: 5.182

8.  Muscle metabolic energy costs while modifying propulsive force generation during walking.

Authors:  Richard E Pimentel; Noah L Pieper; William H Clark; Jason R Franz
Journal:  Comput Methods Biomech Biomed Engin       Date:  2021-03-22       Impact factor: 1.763

Review 9.  Perspective on musculoskeletal modelling and predictive simulations of human movement to assess the neuromechanics of gait.

Authors:  Friedl De Groote; Antoine Falisse
Journal:  Proc Biol Sci       Date:  2021-03-03       Impact factor: 5.349

10.  Task-dependent recruitment across ankle extensor muscles and between mechanical demands is driven by the metabolic cost of muscle contraction.

Authors:  Adrian K M Lai; Taylor J M Dick; Andrew A Biewener; James M Wakeling
Journal:  J R Soc Interface       Date:  2021-01-06       Impact factor: 4.118

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